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In recent years Deep Learning has brought about a breakthrough in Medical Image Segmentation. In this regard, U-Net has been the most popular architecture in the medical imaging community. Despite outstanding overall performance in segmenting multimodal medical images, through extensive experimentations on some challenging datasets, we demonstrate that the classical U-Net architecture seems to be lacking in certain aspects. Therefore, we propose some modifications to improve upon the already state-of-the-art U-Net model. Following these modifications, we develop a novel architecture, MultiResUNet, as the potential successor to the U-Net architecture. We have tested and compared MultiResUNet with the classical U-Net on a vast repertoire of multimodal medical images. Although only slight improvements in the cases of ideal images are noticed, remarkable gains in performance have been attained for the challenging ones. We have evaluated our model on five different datasets, each with their own unique challenges, and have obtained a relative improvement in performance of 10.15%, 5.07%, 2.63%, 1.41%, and 0.62% respectively. We have also discussed and highlighted some qualitatively superior aspects of MultiResUNet over classical U-Net that are not really reflected in the quantitative measures. [ABSTRACT FROM AUTHOR]

Highlights • We present an elegant feature engineering scheme by exploiting the characteristics of a ventricular fibrillation class ECG signal. • Subsequently, we apply machine learning techniques and develop and robust predictor called VFPred. • VFPred thus is a fusion of both signal processing and machine learning techniques. • We have conducted extensive experiments and according to the experimental results, VFPred outperforms the state of the art. Abstract Ventricular Fibrillation (VF), one of the most dangerous arrhythmias, is responsible for sudden cardiac arrests. Thus, various algorithms have been developed to predict VF from electrocardiogram (ECG), which is a binary classification problem. In the literature, we find a number of algorithms based on signal processing, where, after some robust mathematical operations the decision is given based on a predefined threshold over a single value. On the other hand, some machine learning based algorithms are also reported in the literature; however, these algorithms merely combine some parameters and make a prediction using those as features. Both the approaches have their perks and pitfalls; thus our motivation was to coalesce them to get the best out of the both worlds. Hence we have developed, VFPred that, in addition to employing a signal processing pipeline, namely, Empirical Mode Decomposition and Discrete Fourier Transform for useful feature extraction, uses a Support Vector Machine for efficient classification. VFPred turns out to be a robust algorithm as it is able to successfully segregate the two classes with equal confidence (sensitivity = 99.99%, specificity = 98.40%) even from a short signal of 5 s long, whereas existing works though requires longer signals, flourishes in one but fails in the other. [ABSTRACT FROM AUTHOR]

In this paper, an algorithm is proposed that detects the existence of common ancestor gene sequences for non-overlapping inversion (reversed complement) metric given two input DNA sequences. Theoretical worst case running time complexity of the algorithm is proven to be O ( n 4 ) , where n is the length of each input sequence. However, by experiment, the running time complexity is found to be O ( n 3 ) for the worst case and O ( n 2 ) for average case. Moreover, the worst case occurs when both input sequences have the similarity of around 90% which is very rare. This work is motivated by the purpose of diagnosing an unknown genetic disease that shows allelic heterogeneity , a case where a normal gene mutates in different orders resulting in two different gene sequences causing two different genetic diseases. Our algorithm can potentially save huge energy and cost of the existing diagnostic approaches. The algorithm can be useful as well in the study of breed-related hereditary conditions to determine the genetic spread of a defective gene in the population. [ABSTRACT FROM AUTHOR]

In this paper, we study several variations of the pancake flipping problem , which is also well known as the problem of sorting by prefix reversals . We consider the variations in the sorting process by adding with prefix reversals other similar operations such as prefix transpositions and prefix transreversals. We first study the problem of sorting unsigned permutations by prefix reversals and prefix transpositions and present a 3-approximation algorithm for this problem. Then we give a 2-approximation algorithm for sorting by prefix reversals and prefix transreversals. We also provide a 3-approximation algorithm for sorting by prefix reversals and prefix transpositions where the operations are always applied at the unsorted suffix of the permutation. We further analyze the problem from practical point of view and show quantitatively how approximation ratios of our algorithms improve with the increase of number of prefix reversals applied by optimal algorithms. Finally, we present experimental results to support our analysis. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present Dirac-type sufficient conditions for a bipartite graph to possess a Hamiltonian path. We also define a seemingly new family of bipartite graphs, which we call the graphs. We show that our sufficient condition can also ensure Hamiltonicity unless the graph is a graph. [Copyright &y& Elsevier]

Abstract: In this paper, we study the approximate string matching problem under a string distance whose edit operations are translocations of equal length factors. We extend a graph-theoretic approach proposed by Rahman and Illiopoulos (2008) to model our problem. In the sequel, we devise efficient algorithms based on this model to solve a number of variants of the string matching problem with translocations. [Copyright &y& Elsevier]

• Proposed dataset is one of the largest in blood cell segmentation domain. • Learning based algorithm outperforms non-learning based methods in both qualitative and quantitative aspect. • Although Otsu's method demonstrated competitive results in blood segmentation, but it failed to segment low contrast images. With the recent developments in deep learning, automatic cell segmentation from images of microscopic examination slides seems to be a solved problem as recent methods have achieved comparable results on existing benchmark datasets. However, most of the existing cell segmentation benchmark datasets either contain a single cell type, few instances of the cells, not publicly available. Therefore, it is unclear whether the performance improvements can generalize on more diverse datasets. In this paper, we present a large and diverse cell segmentation dataset BBBC041Seg 1 1 https://github.com/Deponker/Blood-cell-segmentation-dataset , which consists both of uninfected cells (i.e., red blood cells/RBCs, leukocytes) and infected cells (i.e., gametocytes, rings, trophozoites, and schizonts). Additionally, all cell types do not have equal instances, which encourages researchers to develop algorithms for learning from imbalanced classes in a few shot learning paradigm. Furthermore, we conduct a comparative study using both classical rule-based and recent deep learning state-of-the-art (SOTA) methods for automatic cell segmentation and provide them as strong baselines. We believe the introduction of BBBC041Seg will promote future research towards clinically applicable cell segmentation methods from microscopic examinations, which can be later used for downstream tasks such as detecting hematological diseases (i.e., malaria). [ABSTRACT FROM AUTHOR]

Abstract: Given two permutations A and B of and a fixed constant c, we introduce the notion of a longest common almost increasing subsequence (LCAIS) as a longest common subsequence that can be converted to an increasing subsequence by possibly adding a value, that is at most c, to each of the elements. We present an algorithm for computing LCAIS in space, time. [Copyright &y& Elsevier]

Abstract: Regularities in indeterminate strings have recently been a matter of interest because of their use in the fields of molecular biology, musical text analysis, cryptanalysis and so on. In this paper, we study the problem of reconstructing an indeterminate string from a border array. We present two efficient algorithms to reconstruct an indeterminate string from a valid border array – one using an unbounded alphabet and the other using minimum sized alphabet. We also propose an algorithm for reconstructing an indeterminate string from suffix array and LCP array. [Copyright &y& Elsevier]

Abstract: Given a pattern P of length n and a text T of length m, the permutation matching problem asks whether any permutation of P occurs in T. Indexing a string for permutation matching seems to be quite hard in spite of the existence of a simple non-indexed solution. In this paper, we devise several time data structures for a binary string capable of answering permutation queries in time. In particular, we first present two time data structures and then improve the data structure construction time to . The space complexity of the data structures remains linear. [Copyright &y& Elsevier]

Abstract: In this paper, we present linear-time algorithms for the construction two novel types of finite automata and show how they can be used to efficiently solve the Longest Common Subsequence (LCS), Shortest Common Supersequence (SCS) and Constrained Longest Common Subsequence (CLCS) problems for degenerate strings. [Copyright &y& Elsevier]

• Proposed malaria dataset exhibits higher variation in uninfected class than the existing public dataset. • Transfer learning on medical images outperforms transfer learning on the natural image domain. • Conditional image synthesis can address the problem of malaria data imbalance. • Training on a high variation dataset yields better performance on data from a different domain. Malaria, one of the leading causes of death in underdeveloped countries, is primarily diagnosed using microscopy. Computer-aided diagnosis of malaria is a challenging task owing to the fine-grained variability in the appearance of some uninfected and infected class. In this paper, we transform a malaria parasite object detection dataset into a classification dataset, making it the largest malaria classification dataset (63,645 cells), and evaluate the performance of several state-of-the-art deep neural network architectures pretrained on both natural and medical images on this new dataset. We provide detailed insights into the variation of the dataset and qualitative analysis of the results produced by the best models. We also evaluate the models using an independent test set to demonstrate the model's ability to generalize in different domains. Finally, we demonstrate the effect of conditional image synthesis on malaria parasite detection. We provide detailed insights into the influence of synthetic images for the class imbalance problem in the malaria diagnosis context. [ABSTRACT FROM AUTHOR]

The transit network design problem is concerned with the finding of a set of routes with corresponding schedules for a public transport system. This problem belongs to the class of NP-Hard problem because of the vast search space and multiple constraints whose optimal solution is really difficult to find out. The paper develops a Population based model for the transit network design problem. While designing the transit network, we give preference to maximize the number of satisfied passengers, to minimize the total number of transfers, and to minimize the total travel time of all served passengers. Our approach to the transit network design problem is based on the Genetic Algorithm (GA) optimization. The Genetic Algorithm is similar to evolution strategy which iterates through fitness assessment, selection and breeding, and population reassembly. In this paper, we will show two different experimental results performed on known benchmark problems. We clearly show that results obtained by Genetic Algorithm with increasing population is better than so far best technique which is really difficult for future researchers to beat. [ABSTRACT FROM AUTHOR]

Accurate location-based groundwater potential mapping for recharge is a vital tool to infer simple, efficient groundwater management in policy level planning. In this context, this study introduces a novel metaheuristic based multiobjective optimization model, namely, CIMCA for preparing groundwater accumulation maps in parallel to analyzing four different Multi-Criteria Analysis (MCA) techniques. In CIMCA, computational intelligence (CI) based optimization is infused within the traditional MCA techniques, which is carefully guided/influenced by the domain expert's input within the MCA part. The CIMCA model has been found to exploit the best of both worlds, i.e., optimization and domain expert's influence, thereby achieving unbiased and consistent outputs having great prospects for mapping potential groundwater resources for sustainable planning. Takeaways from these comparative assessments, impact information at district and sub-district levels, exposure detailing on total numbers of pumping wells, etc., will be useful in formulating current planning and devising future strategies under the umbrella of sustainable groundwater management. • We present and analyze models to assess groundwater potential. • We introduce CIMCA, a novel metaheuristic based multiobjective optimization model. • We analyze multi-criteria analysis (MCA) techniques. • CIMCA infuses computational intelligence (CI) based optimization within MCA. • The optimization in CIMCA is carefully guided by the domain expert's input. [ABSTRACT FROM AUTHOR]

Abstract Nuclear Magnetic Resonance Spectroscopy (most commonly known as NMR Spectroscopy) is used to generate approximate and partial distances between pairs of atoms of the native structure of a protein. To predict protein structure from these partial distances by solving the Euclidean distance geometry problem from the partial distances obtained from NMR Spectroscopy, we can predict three-dimensional (3D) structure of a protein. In this paper, a new genetic algorithm is proposed to efficiently address the Euclidean distance geometry problem towards building 3D structure of a given protein applying NMR's sparse data. Our genetic algorithm uses (i) a greedy mutation and crossover operator to intensify the search; (ii) a twin removal technique for diversification in the population; (iii) a random restart method to recover from stagnation; and (iv) a compaction factor to reduce the search space. Reducing the search space drastically, our approach improves the quality of the search. We tested our algorithms on a set of standard benchmarks. Experimentally, we show that our enhanced genetic algorithms significantly outperforms the traditional genetic algorithms and a previously proposed state-of-the-art method. Our method is capable of producing structures that are very close to the native structures and hence, the experimental biologists could adopt it to determine more accurate protein structures from NMR data. [ABSTRACT FROM AUTHOR]

This paper presents a number of novel metaheuristic approaches that can efficiently map stream graphs on multicores. A stream graph consists of a set of actors performing different functions communicating through edges. Orchestrating stream graphs on multicores can be formulated as an Integer Linear Programming (ILP) problem but ILP solver takes exponential time to provide an optimal solution. We propose metaheuristic algorithms to achieve near optimal solutions within a reasonable amount of time. We employ six different variants of the Hill-Climbing (HC) algorithm employing different tweak operators that produce excellent result extremely quickly. We also propose six different variants of Genetic Algorithm (GA) to examine how effective these variants can be in escaping the local optima. We finally combine HC and GA techniques (which is also known as ‘ memetic algorithm ’) to produce hybrid techniques that outperform the individual performance of HC and GA techniques. We compare our results with the results generated by the CPLEX optimization tool. Our best technique has achieved a geometric mean speedup of 7.42× across a range of StreamIt benchmarks on an eight-core processor. [ABSTRACT FROM AUTHOR]

Traditional clustering algorithms often exhibit poor performance for large networks. On the contrary, greedy algorithms are found to be relatively efficient while uncovering functional modules from large biological networks. The quality of the clusters produced by these greedy techniques largely depends on the underlying heuristics employed. Different heuristics based on different attributes and properties perform differently in terms of the quality of the clusters produced. This motivates us to design new heuristics for clustering large networks. In this paper, we have proposed two new heuristics and analyzed the performance thereof after incorporating those with three different combinations in a recently celebrated greedy clustering algorithm named SPICi. We have extensively analyzed the effectiveness of these new variants. The results are found to be promising. [ABSTRACT FROM AUTHOR]

Multiple sequence alignment (MSA) is a prerequisite for several analyses in bioinformatics, such as, phylogeny estimation, protein structure prediction, etc. PASTA (Practical Alignments using SATé and TrAnsitivity) is a state-of-the-art method for computing MSAs, well-known for its accuracy and scalability. It iteratively co-estimates both MSA and maximum likelihood (ML) phylogenetic tree. It attempts to exploit the close association between the accuracy of an MSA and the corresponding tree while finding the output through multiple iterations from both directions. Currently, PASTA uses the ML score as its optimization criterion which is a good score in phylogeny estimation but cannot be proven as a necessary and sufficient criterion to produce an accurate phylogenetic tree. Therefore, the integration of multiple application-aware objectives into PASTA, which are carefully chosen considering their better association to the tree accuracy, may potentially have a profound positive impact on its performance. This paper has employed four application-aware objectives alongside ML score to develop a multi-objective (MO) framework, namely, PMAO that leverages PASTA to generate a bunch of high-quality solutions that are considered equivalent in the context of conflicting objectives under consideration. our experimental analysis on a popular biological benchmark reveals that the tree-space generated by PMAO contains significantly better trees than stand-alone PASTA. To help the domain experts further in choosing the most appropriate tree from the PMAO output (containing a relatively large set of high-quality solutions), we have added an additional component within the PMAO framework that is capable of generating a smaller set of high-quality solutions. Finally, we have attempted to obtain a single high-quality solution without using any external evidences and have found that summarizing the few solutions detected through the above component can serve this purpose to some extent. [Display omitted] • PMAO framework integrates many application-aware objectives into PASTA through multi-objective optimization for better phylogeny estimation. • We innovatively employ supervised machine learning as well as some simple criteria within the PMAO framework to assist the domain expert. • We experiment with summarizing the PMAO output trees to obtain a single high-quality solution without using any external evidence. [ABSTRACT FROM AUTHOR]

• Understanding the characteristics of spatiotemporal clustering of the COVID-19 epidemic and R0 is critical in effectively preventing and controlling the pandemic. • Temperature profile played a significant role in the spatiotemporal clustering of the COVID-19 epidemic in China • The increase in temperature increases the R0 value • Temperatures had more contribution towards the transmission of COVID-19 than population age in China. The COVID-19 epidemic spread rapidly through China and subsequently proliferated globally leading to a pandemic situation around the globe. Human-to-human transmission, as well as asymptomatic transmission of the infection, have been confirmed. As of April 03, 2020, public health crisis in China due to COVID-19 was potentially under control. We compiled a daily dataset of case counts, mortality, recovery, temperature, population density, and demographic information for each prefecture during the period of January 11 to April 07, 2020. Understanding the characteristics of spatial clustering of the COVID-19 epidemic and R 0 is critical in effectively preventing and controlling the ongoing global pandemic. Considering this, the prefectures were grouped based on several relevant features using unsupervised machine learning techniques. Subsequently, we performed a computational analysis utilizing the reported cases in China to estimate the revised R 0 among different regions. Finally, our overall research indicates that the impact of temperature and demographic factors on virus transmission may be characterized using a stochastic transmission model. Such predictions will help in prevention planning in an ongoing global pandemic, prioritizing segments of a given community/region for action and providing a visual aid in designing prevention strategies for a specific geographic region. Furthermore, revised estimation and our methodology will aid in improving the human health consequences of COVID-19 elsewhere. [ABSTRACT FROM AUTHOR]